Utility of Comprehensive Serum Glycopeptide Spectra Analysis (CSGSA) for the Detection of Early Stage Epithelial Ovarian Cancer.
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ABSTRACT:
SUBMITTER: Matsuo K
PROVIDER: S-EPMC7563232 | biostudies-literature | 2020 Aug
REPOSITORIES: biostudies-literature
Publications
Utility of Comprehensive Serum Glycopeptide Spectra Analysis (CSGSA) for the Detection of Early Stage Epithelial Ovarian Cancer.
Matsuo Koji K Tanabe Kazuhiro K Hayashi Masaru M Ikeda Masae M Yasaka Miwa M Machida Hiroko H Shida Masako M Sato Kenji K Yoshida Hiroshi H Hirasawa Takeshi T Imanishi Tadashi T Mikami Mikio M
Cancers 20200821 9
Comprehensive serum glycopeptide spectra analysis (CSGSA) evaluates >10,000 serum glycopeptides and identifies unique glycopeptide peaks and patterns via supervised orthogonal partial least-squares discriminant modeling. CSGSA was more accurate than cancer antigen 125 (CA125) or human epididymis protein 4 (HE4) for detecting early stage epithelial ovarian cancer. Combined CSGSA, CA125, and HE4 had improved diagnostic performance. Thus, CSGSA may be a useful screening tool for detecting early sta ...[more]
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Project description:ObjectivesTo conduct a comprehensive glycopeptide spectra analysis of serum between cancer and non-cancer patients to identify early biomarkers of epithelial ovarian cancer (EOC).MethodsApproximately 30,000 glycopeptide peaks were detected from the digested serum glycoproteins of 39 EOC patients (23 early-stage, 16 advanced-stage) and 45 non-cancer patients (27 leiomyoma and ovarian cyst cases, 18 endometrioma cases) by liquid chromatography mass spectrometry (LC-MS). The differential glycopeptide peak spectra were analyzed to distinguish between cancer and non-cancer groups by employing multivariate analysis including principal component analysis (PCA), orthogonal partial least squares discriminant analysis (OPLS-DA) and heat maps.ResultsExamined spectral peaks were filtered down to 2281 serum quantitative glycopeptide signatures for differentiation between ovarian cancer and controls using multivariate analysis. The OPLS-DA model using cross-validation parameters R2 and Q2 and score plots of the serum samples significantly differentiated the EOC group from the non-cancer control group. In addition, women with early-stage clear cell carcinoma and endometriomas were clearly distinguished from each other by OPLS-DA as well as by PCA and heat maps.ConclusionsOur study demonstrates the potential of comprehensive serum glycoprotein analysis as a useful tool for ovarian cancer detection.
| S-EPMC6563019 | biostudies-literature
Project description:Ovarian cancer is a leading cause of deaths among gynecological cancers, and a method to detect early-stage epithelial ovarian cancer (EOC) is urgently needed. We aimed to develop an artificial intelligence (AI)-based comprehensive serum glycopeptide spectra analysis (CSGSA-AI) method in combination with convolutional neural network (CNN) to detect aberrant glycans in serum samples of patients with EOC. We converted serum glycopeptide expression patterns into two-dimensional (2D) barcodes to let CNN learn and distinguish between EOC and non-EOC. CNN was trained using 60% samples and validated using 40% samples. We observed that principal component analysis-based alignment of glycopeptides to generate 2D barcodes significantly increased the diagnostic accuracy (88%) of the method. When CNN was trained with 2D barcodes colored on the basis of serum levels of CA125 and HE4, a diagnostic accuracy of 95% was achieved. We believe that this simple and low-cost method will increase the detection of EOC.
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Project description:Epithelial ovarian cancer (EOC) is the sixth most common cause of cancer deaths in women because the diagnosis occurs mostly when the disease is in its late-stage. Current diagnostic methods of EOC show only a moderate sensitivity, especially at an early-stage of the disease; hence, novel biomarkers are needed to improve the diagnosis. We recently reported that serum glycome modifications observed in late-stage EOC patients by MALDI-TOF-MS could be combined as a glycan score named GLYCOV that was calculated from the relative areas of the 11 N-glycan structures that were significantly modulated. Here, we evaluated the ability of GLYCOV to recognize early-stage EOC in a cohort of 73 individuals comprised of 20 early-stage primary serous EOC, 20 benign ovarian diseases (BOD), and 33 age-matched healthy controls. GLYCOV was able to recognize stage I EOC whereas CA125 values were statistically significant only for stage II EOC patients. In addition, GLYCOV was more sensitive and specific compared to CA125 in distinguishing early-stage EOC from BOD patients, which is of high relevance to clinicians as it is difficult for them to diagnose malignancy prior to operation.
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Project description:We aimed to determine prognostic factors of early stage (I/II) epithelial ovarian carcinoma (EOC) including clinicopathologic and chemotherapeutic regimens. Four hundred and thirty-seven women who underwent primary staging surgery with adjuvant chemotherapy between January 1, 2000 and December 31, 2010 were retrospectively reviewed and analyzed from two medical centers. The prognostic factors were determined from multivariate survival analyses using Cox regression models. The majority of women were diagnosed with stage Ic (244/437, 55.8%). The histopathologic types were clear cell (37.5%), endometrioid (27.2%), serous (14.0%), and mucinous (13.3%). Fifty-seven percent (249/437) of the women received taxane-based (platinum plus paclitaxel) regimens and 43.0% received non-taxane (platinum plus cyclophosphamide) regimens as frontline adjuvant chemotherapy. Clear cell tumors (adjusted Hazard ratio (aHR) 0.37, 95% confidence interval (CI) 0.21⁻0.73, p = 0.001) showed better 5-year disease-free survival (DFS) than serous tumors. Women diagnosed at FIGO (International Federation of Gynecology and Obstetrics) stage II (aHR 5.97, 95% CI = 2.47⁻14.39, p < 0.001), grade 3 tumor without clear cell (aHR 2.28, 95% CI = 1.02⁻5.07, p = 0.004) and who received 3⁻5 cycles of non-taxane regimens (aHR 3.29, 95% CI = 1.47⁻7.34, p = 0.004) had worse 5-year overall survival (OS). Clear cell histology treated with taxane-based regimens showed significantly higher 5-year DFS (91.2% vs. 82.0%, aHR = 0.45, 95% CI = 0.21⁻0.93, p = 0.043) and 5-year OS (93.5% vs. 79.0%, aHR = 0.30, 95% CI = 0.13⁻0.70, p = 0.005) than those treated with non-taxane-based regimens. We conclude that stage, tumor grade, and chemotherapeutic regimens/cycles are independent prognostic factors for early stage ovarian cancer.
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Project description:The aim of the present study was to identify the appropriate DNA sequence and design high-quality primers for methylation-specific polymerase chain reaction (MSP). These primers may be used to examine and identify patients with early-stage epithelial ovarian carcinoma (EOC). Opioid binding protein/cell adhesion molecule like (OPCML), Runt-related transcription factor 3 and tissue factor pathway inhibitor 2 were selected as possible molecular markers. MSP primer sets were designed to monitor the methylation of the three markers. Free circulating DNA (fcDNA) from 194 patients with epithelial ovarian carcinoma and healthy donors were templates in the nested MSP. OPCML MSP was effective with respect to screening methylated fcDNA. One-way ANOVA P-values indicated that the difference in cancer antigen 125 (CA125), a biomarker for EOC diagnosis, level between early EOC and healthy donors was not significant. The methylation of OPCML was significantly altered in early-stage EOC compared with healthy donors (P<0.0001), and this supported the hypothesis that specific fcDNA methylation was able to distinguish patients with early-stage EOC from healthy donors. With respect to detecting early EOC, compared with the results of the CA125 test, MSP increased the κ coefficient from 0.140 to 0.757. Therefore, OPCML combined with fcDNA may be used to establish an improved clinical assay compared with the current CA125 test.
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Project description:FDA-cleared ovarian cancer biomarkers are limited to CA-125 and HE4 for monitoring and recurrence and OVA1, a multivariate panel consisting of CA-125 and four additional biomarkers, for referring patients to a specialist. Due to relatively poor performance of these tests, more accurate and broadly applicable biomarkers are needed. We evaluated the dysregulation of 259 candidate cancer markers in serum samples from 499 patients. Sera were collected prospectively at 11 monitored sites under a single well-defined protocol. All stages of ovarian cancer and common benign gynecological conditions were represented. To ensure consistency and comparability of biomarker comparisons, all measurements were performed on a single platform, at a single site, using a panel of rigorously calibrated, qualified, high-throughput, multiplexed immunoassays and all analyses were conducted using the same software. Each marker was evaluated independently for its ability to differentiate ovarian cancer from benign conditions. A total of 175 markers were dysregulated in the cancer samples. HE4 (AUC=0.933) and CA-125 (AUC=0.907) were the most informative biomarkers, followed by IL-2 receptor ?, ?1-antitrypsin, C-reactive protein, YKL-40, cellular fibronectin, CA-72-4 and prostasin (AUC>0.800). To improve the discrimination between cancer and benign conditions, a simple multivariate combination of markers was explored using logistic regression. When combined into a single panel, the nine most informative individual biomarkers yielded an AUC value of 0.950, significantly higher than obtained when combining the markers in the OVA1 panel (AUC 0.912). Additionally, at a threshold sensitivity of 90%, the combination of the top 9 markers gave 88.9% specificity compared to 63.4% specificity for the OVA1 markers. Although a blinded validation study has not yet been performed, these results indicate that alternative biomarker combinations might lead to significant improvements in the detection of ovarian cancer.
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Project description:High performance mass spectrometry was employed to interrogate the serum metabolome of early-stage ovarian cancer (OC) patients and age-matched control women. The resulting spectral features were used to establish a linear support vector machine (SVM) model of sixteen diagnostic metabolites that are able to identify early-stage OC with 100% accuracy in our patient cohort. The results provide evidence for the importance of lipid and fatty acid metabolism in OC and serve as the foundation of a clinically significant diagnostic test.
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Project description:CA125 as a biomarker of ovarian cancer is ineffective for the general population. The aim of this study was to evaluate multiplexed bead-based immunoassay of multiple ovarian cancer-associated biomarkers such as transthyretin and apolipoprotein A1, together with CA125, to improve the identification and evaluation of prognosis of ovarian cancer. We measured the serum levels of CA125, transthyretin, and apolipoprotein A1 from the serum of 61 healthy individuals, 84 patients with benign ovarian disease, and 118 patients with ovarian cancer using a multiplex liquid assay system, Luminex 100. The results were then analyzed according to healthy and/or benign versus ovarian cancer subjects. When CA125 was combined with the other biomarkers, the overall sensitivity and specificity were significantly improved in the ROC curve, which showed 95% and 97% sensitivity and specificity, respectively. At 95% specificity for all stages the sensitivity increased to 95.5% compared to 67% for CA125 alone. For stage I+II, the sensitivity increased from 30% for CA125 alone to 93.9%. For stage III+IV, the corresponding values were 96.5% and 91.6%, respectively. Also, the three biomarkers were sufficient for maximum separation between noncancer (healthy plus benign group) and stage I+II or all stages (I-IV) of disease. The new combination of transthyretin, and apolipoprotein A1 with CA125 improved both the sensitivity and the specificity of ovarian cancer diagnosis compared with those of individual biomarkers. These findings suggest the benefit of the combination of these markers for the diagnosis of ovarian cancer.
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